Field
Various embodiments disclosed herein relate to an implementation for a force feedback method of an exoskeleton system, with applications in augment reality or virtual reality and robot control.
Description of Related Art
There are currently a number of hand-motion capturing solutions. Some existing solutions use camera and computer vision technologies to analyze the shape and direction of a hand. Three hand-motion-capturing approaches use optical capturing, IMU (inertia measurement unit) capturing and bending sensor (i.e. Flex sensors) capturing.
Among devices that use the optical capturing approach, a device called the “Leap Motion Controller”, from Leap Motion, Inc., uses an infrared camera to track motion of hands and utilizes algorithms to fuse data from the camera. However, some disadvantages are commonly seen in devices that use computer vision to track the motion of the hand. The ability of these devices to track hand motion is restricted due to the camera's limited monitoring scope and direction. These devices are not capable of generating a correct hand model when a user's hand is out of the camera's monitoring scope. Furthermore, an optical-capturing-based solution is unable to offer force feedback without incorporating an additional wearable device.
Another device called the “Control VR”, from Control VR, uses IMU to measure the offset angle of each finger. A disadvantage of the IMU device is the need to recalibrate the zero-offset each time the device is placed in a new magnetic environment, and that it loses accurate tracking when placed in a strong magnetic field. Additionally, because this approach involves installing drivers on the back of the user's hand, implementing force feedback is more difficult. Moreover, this approach uses bending sensors, such as a strain gauge, installed on a glove to capture hand motion. However, this approach is not able to accurately provide measurements because of the non-linear relationship between sensor readings and bending of finger. As well, it is difficult to implement force-feedback with this approach due to similar reasons as with the IMU approach. Bending sensors based approach, similar to the IMU approach, is unable to offer exact coordinates to describe finger positions due to their principles of measurements.
Early attempts to implement haptic interfaces for human hands include the PHANTOM, which measures users' hand position with a grounded robotic arm and exerts controlled point force vector on users' hand. PHANTOM achieved precise stiffness control by adjusting the torque of three DC brushed motors with encoders. This technology is essentially a transmission between the motors and the human hand. Therefore the workspace for the user and the mobility is highly limited. Moreover, this system fails to produce feedback for individual fingers, reducing the credibility of the haptic experience. The Rutgers Master II ND utilizes pneumatic actuators arranged in center of palm and achieves force feedback by directly driving the fingers. This device uses the non-contact Hall effect and IR sensors for motion capturing for durability reasons, yet this approach raises manufacture costs. Specifications of the RMII-ND haptic glove are comparable to those of the CyberGrasp, another well-known haptic glove system. CyberGrasp uses resistive bend sensors for motion capturing. This system uses a DC motor and cable-pulley transmissions on an exoskeleton to pull users' finger backward in order to simulate the exerted force. Primarily intended for corporations, such as military and medical rehabilitation, CyberGrasp system is not a consumer-grade product. While these two systems are capable of offering precise force control, they are large in size and expansive due to the complexity of the design. Other systems include Haptic Telexistence, HIRO III (Japanese robotics system) and RML Glove.